Top Hybrid Machine Learning Engineer Jobs

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.

Paid 12-week hybrid Ph.D. research internship conducting original ML research for world models and autonomous driving. Responsibilities include designing, training, and evaluating models (world modeling, model-based RL, 3D perception), prototyping in closed-loop simulation and hardware, working with large datasets, collaborating with researchers, and publishing results.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

Develop and productionize ML-driven physics simulation capabilities: experiment with temporal propagation, generate and curate training/verification data, iterate prototypes to production, collaborate with physicists, AI researchers, engineers, and customers to scale transient PDE and dynamics modeling.

Build and improve AI/ML systems and data pipelines for a B2B SaaS platform; shape product and technical strategy; work with customers; mentor team members; ensure secure, robust, and scalable infrastructure.